In the field of information technology, various types of metrics data, including numerical and unstructured metrics data, for applications and networks are collected in order to monitor application and network performance. When performance degradation occurs, these collected metrics may be analyzed via correlation to diagnose probable root cause(s). Correlating the collected metrics may allow for the identification of the metrics most correlated to a problematic metric associated with the performance degradation. These most correlated metrics may likely be associated with the probable root cause(s). However, as the number of applications and sampled data for disparate metrics collected per application increases, traditional correlation techniques become less reliable and determining the most correlated metrics becomes exceedingly computationally prohibitive. Beyond the sheer and increasing number of application metrics, applications are also operating on increasingly finer-grained data, such as finer time resolutions for performance data. This finer-grained data further increases the amount of sampled data, which may exacerbate computational complexity needed to perform correlation.
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